knitr::opts_chunk$set(echo = TRUE)

# Load libraries for homework problems
library(tidyverse)
library(gt)
library(patchwork)

# Read in COVID-19 data
# R/make_data.R creates this file
cv19 <- read_csv('data/usa_covid19.csv')

Overview

The COVID-19 pandemic is an ongoing public health emergency in the United States (US) and worldwide. Since 2020-01-21, the New York times has monitored and shared COVID-19 data (see github repo here) from across the US at the state and county level.

Data dictionary

I have modified the New York times data to include information about state’s population levels. The data are described below:

c("date" = "Date", 
  "state" =  "State in the US", 
  "cases_total" = "Total number of cases as of date", 
  "deaths_total" = "Total number of deaths as of date",
  "pop_2015" = "Estimated population as of 2015"
) %>% 
  enframe() %>% 
  gt(rowname_col = "name") %>%
  tab_stubhead(label = 'Variable name') %>% 
  cols_label(value = 'Variable description') %>% 
  cols_align('right') %>% 
  tab_footnote(locations = cells_body(rows = 5, columns = 2),
    footnote = "Source: usmap::countypop") %>% 
  tab_footnote(locations = cells_body(columns = 2, rows = 2), 
    footnote = 'US = United States') %>% 
  tab_header(title = 'Dictionary for New York Times COVID-19 data',
    subtitle = paste("Last updated:", max(cv19$date)))

Dictionary for New York Times COVID-19 data
Last updated: 2020-06-03
Variable name	Variable description
date	Date
state	State in the US¹
cases_total	Total number of cases as of date
deaths_total	Total number of deaths as of date
pop_2015	Estimated population as of 2015²
¹ US = United States ² Source: usmap::countypop

Data pages

The data (cv19) are printed below:

cv19

Problem 1

Create two new columns in cv19:

cases_new the number of new cases identified on a given day for a given state.
deaths_new the number of new deaths confirmed on a given day for a given state.

Notes:

the lag() function is helpful for this.
Your solution should look like this

read_rds('solutions/01_solution.rds')

Problem 2

Compute the total number of new cases identified and deaths confirmed each day in the USA on or after March 1st, 2020. Your summarized data should look like this:

read_rds('solutions/02_solution.rds')

Problem 3

Using the data created in problem 2, create two bar plots showing the number of new cases identified and deaths confirmed in the USA after March 1st, 2020.

Notes This is a great chance to learn about the patchwork R package.

Your solution should look like this

read_rds('solutions/03_solution.rds')

Problem 4

Using the data from problem 1, create the table below:

read_rds('solutions/04_solution.rds')

Ten states in the US with highest death rates due to COVID-19
Data presented for: 2020-06-03
	Cases			Deaths
	Total count	Rate per 100k	Moving average ratio	Total count	Rate per 100k	Moving average ratio
New York	378,924	1,914.2	0.9	29,918	151.1	0.7
New Jersey	162,068	1,809.2	0.9	11,880	132.6	0.9
Connecticut	43,091	1,200.0	0.8	3,989	111.1	0.7
Massachusetts	101,592	1,495.2	1.4	7,152	105.3	1.3
District of Columbia	9,016	1,341.2	0.7	473	70.4	0.7
Rhode Island	15,219	1,440.8	0.9	742	70.2	0.7
Louisiana	41,244	883.0	0.8	2,870	61.4	1.3
Michigan	58,990	594.5	1.3	5,579	56.2	0.9
Pennsylvania	77,871	608.2	0.8	5,742	44.9	0.9
Illinois	124,279	966.4	0.7	5,665	44.1	1.0

Hints:

To create the dataset that will generate this table, you will likely need to - filter the data from problem 1 to contain the 2 most recent weeks.

derive the following columns:
- cases_per100k: Number of cases per 100,000 citizens
- deaths_per100k: Number of deaths per 100,000 citizens
- cases_mar: Number of days until case count doubles, based on current day’s case count
- deaths_mar: Number of days until death count doubles, based on current day’s death count.
filter your data down to the 10 states that have the highest death rate per 100,000 citizens.

Problem 5

Learn something new: take a look at a famous flipbook created by Gina Reynolds. The cv19 data have a very similar structure to that of the flipbook in Gina’s talk. Learn about the ggplot2 tools that are used in the flipbook and try to adapt them to create the ‘racing bar chart’ below.

Homework 2

[YOUR NAME HERE]

2020-06-04